Malaria infection led to 584,000 deaths in 2014, and despite disease control efforts; 3.3 billion people remain at risk of infection. These control measures have included both conservative strategies as well as more aggressive approaches. While these methods have made in-roads in managing the global malaria epidemic, there is a great need for sustainable, effective diagnostic tools to help direct elimination campaigns. One such approach is the test-and-treat elimination strategy. In Zambia, at the local level, test and treat has drastically lowered disease incidence in settings with low malaria transmission. In these efforts, a malaria- positive rapid diagnostic test (RDT) initiates a reactive-case management response. This involves a dated, even by low and middle-income countries standards, communication chain that ultimately results in a community healthcare worker traveling to the home of the index case patient and testing those at risk of infection within a specified proximity. Despite its successes, the slow flow of information and the heavy reliance on a large number of volunteer community healthcare workers result in concerns over scalability and sustainability. As a result, this elimination strategy has failed to gain traction among other countries. To overcome these critical challenges, we propose an integrated mHealth-based strategy that will streamline the information workflow, provide efficient resource management, and direct evidenced-based decision-making. Our strategy utilizes mobile phone image-processing to quantitatively interpret RDTs and the built-in reporting capabilities characteristic of mobile devices. The accurate POC analysis will immediately benefit the patient by directing them to the appropriate treatment and improve disease surveillance from the field. This near real-time aggregated data will allow national health officials to make informed decisions that result in more targeted interventions. To achieve these goals, we propose: Specific Aim 1. Development of mobile Health and Treat (mHAT) software. Specific Aim 2. Field evaluation of the mHAT reactive case management strategy. We have assembled a team of malaria researchers from chemistry (Wright), biomedical engineering (Scherr), biomedical informatics (Were), and clinician and field scientists with experience in Zambia (Conrad, Thuma) to address the need for integrated diagnostic tools for malaria elimination. At the individual patient level, we expect our tools will provide patients and community healthcare workers with an accurate and rapid diagnosis; at the community level, we anticipate a more efficient and sustainable strategy. At the national level, outcomes include more effective monitoring and focusing of healthcare specific resources, and the potential for data-driven interventions.